aishwaryanr
b939582394
* Restructure navigation into journeys and topics; backfill research and refresh courses - Add Use/Build/Understand journey pages and topic pages with a 101/201/301 catalog, surfaced through a Journey x Level grid in the README - Redistribute the free-course and notebook lists into the new navigation; add 2025-2026 courses and remove paid or dead entries - Backfill monthly best-papers lists from March 2025 through June 2026 - Extend the RAG, AI evaluation, and agentic search research tables to mid-2026 - Archive the 2024 course and paper material with banners - Fix the citation block and drop stale calls to action * Add all-free-courses-by-topic index; use numerals for numbers - Add courses.md: every free course grouped by topic, linked from the README router and the browse-by-topic section - Write counts as numerals across the navigation (10 not ten, 3-day not three-day) * Redesign the LLM foundations, agents, and RAG roadmaps for 2026 - Foundations roadmap: reorder so agents and evaluation are core days and fine-tuning becomes an optional advanced day; refresh all resources - Agents roadmap: rebuild around the model-plus-harness mental model, tools, context and memory, MCP and multi-agent, and agent evaluation - RAG roadmap: retrieval foundations, building a RAG app, agentic and advanced RAG - Point to current LevelUp materials and verified 2025-2026 resources * Refresh Agents 101 for 2026 and apply LevelUp Labs branding - Rewrite the Agents 101 guide around the model-plus-harness model, MCP, reasoning models, real-world agents (coding/computer-use/deep-research), and modern agent evaluation; drop the BabyAGI-era example and stale benchmarks - Rebrand references to LevelUp Labs and link the team to levelup-labs.ai * Archive the 2024 multimodal guide with a pointer to current material * Point each topic page to the canonical course list in courses.md * Add Harness Engineering path and enrich fine-tuning material - New Harness Engineering path: agent = model + harness, from using Claude Code, Cursor, and Codex to assembling and evaluating your own harness; wired into the Build journey's named paths - Fine-tuning topic: add current 2025-2026 material (post-training courses, the RLHF book, Hugging Face TRL and the LLM course chapter, Unsloth, Axolotl, and a practical 2025 fine-tuning guide) * Add role-based interview prep guide - New Role-Based Interview Prep: maps AI/LLM Engineer, ML/Fine-tuning Engineer, Applied Scientist, AI Product Manager, and Solutions Architect roles to what each interview tests and the repo material to prepare with; anchored on the 60 questions - Linked from the Interview Prep path and the README * Add branded roadmap headers and a real role-based interview question bank - Add LevelUp Labs branded headers for the LLM foundations and AI agents roadmaps (white background, blue, credited), wired into the guides - Rewrite role-based interview prep as an actual question bank with answers for AI/LLM Engineer, ML/Fine-tuning Engineer, Applied Scientist, PM, and Solutions Architect, rather than a navigation index * Add branded 3-Day RAG Roadmap header * Update RAG roadmap header with the branded 3-Day RAG image * Add deep role-based interview prep hub (AI Engineer, AI PM, FDE, AI Strategist) - Full folder per role: overview, interview rounds, a large answered question bank (210 questions across roles), verified free resources and courses, and a prep plan - Grounded in 2025-2026 research on how each role is actually interviewed at named companies; external links verified, cross-links to repo content - Replace the earlier shallow role summary with the hub and per-role folders
6.9 KiB
6.9 KiB
FDE Free Courses
The best free courses for the Forward-Deployed Engineer loop, grouped by topic. Start with this repository's own courses (they are self-contained and current), then use the verified external courses to go deeper. Every external link returns HTTP 200. Free only.
An FDE needs breadth over narrow depth: enough foundations to reason clearly, real fluency in RAG and agents, genuine evaluation skill, and the deployment and reliability mindset. Sequence accordingly.
Start here: this repository's courses
- Agentic AI Crash Course: builds agent fundamentals (tools, memory, loops, orchestration) that the design and agent questions assume. Do this first if agents are your weak spot.
- AI Evals for Everyone: builds evaluation skill, which is the single most-probed AI depth area for FDE roles at OpenAI and Anthropic ("how do you know it works?"). Do not skip this.
- All free courses by topic (repository index): the full catalog to find a course for any gap.
- Structured tracks: Harness Engineering path and Agent Builder path sequence exactly what an AI FDE deploys.
Foundations and the LLM stack
- Hugging Face LLM Course: free, hands-on course across transformers, tokenization, and using and adapting LLMs. Builds the base the foundations questions assume.
- Generative AI for Beginners (Microsoft): a free, lesson-based course covering prompting, RAG, agents, and app-building. Good breadth if you are starting cold.
- AI For Beginners (Microsoft): a free curriculum for the broader ML and AI vocabulary. Foundational anchor; skim for gaps.
- The Full Stack LLM Bootcamp: free lectures on building LLM-powered applications end to end, close to the FDE build loop.
Prompting and context engineering
- ChatGPT Prompt Engineering for Developers (DeepLearning.AI): free short course on practical prompting patterns for building, not just chatting.
- Anthropic prompt engineering interactive tutorial: a free, hands-on, notebook-based course on prompting for production. Builds the reliable-prompting skill the coding and design rounds reward.
- DeepLearning.AI short courses catalog: browse the current free short-course list for prompting, RAG, agents, and evaluation.
RAG
- Anthropic Courses (GitHub): free courses including retrieval and building with Claude, notebook-based and current.
- RAG_Techniques (Nir Diamant): a course-grade, runnable collection covering chunking, hybrid search, reranking, and RAG evaluation. Work through several notebooks; RAG is central to the FDE build and design rounds.
- Repository: the RAG topic page and Agentic RAG 101 read as a compact course.
Agents and MCP
- Hugging Face AI Agents Course: a free, structured, hands-on course on building agents from fundamentals through deployment. Pair with the repository's Agentic AI Crash Course.
- Functions, Tools and Agents with LangChain (DeepLearning.AI): free short course on tool use and function calling, the mechanics behind reliable agents.
- AI Agents in LangGraph (DeepLearning.AI): free short course on building agentic workflows with explicit control flow.
- MCP: Build Rich-Context AI Apps with Anthropic (DeepLearning.AI): free short course on the Model Context Protocol, directly relevant to integrating into a customer's tools and data.
- GenAI_Agents (Nir Diamant): runnable agent tutorials and patterns to practice building.
- Repository: Agents topic and Agents 101 guide.
Evaluation
- AI Evals for Everyone (this repository): the primary course for the evaluation depth FDE loops probe hard. Learn to build a labeled set, validate a judge, and measure reliability.
- Evaluating and Debugging Generative AI (DeepLearning.AI): free short course on experiment tracking, evaluation, and debugging generative systems.
- Repository: Evaluation topic and the AI evaluation 2025 table.
Fine-tuning (know when, and roughly how)
- Repository: Fine-tuning topic and Fine-tuning 101 guide. For FDE you mainly need to defend when to fine-tune versus RAG versus prompt, not to train models daily, so understanding beats hands-on depth here.
- llm-course (Maxime Labonne): free notebooks on fine-tuning and quantization if you want hands-on depth.
Deployment, reliability, and responsible AI
- Google SRE Book: the free reference for the reliability, rollout, and incident-response mindset the design and reliability rounds reward. Foundational anchor; read the chapters on service level objectives, monitoring, and release engineering.
- Repository: Production and LLMOps topic, Safety and Security topic, and Securing Agentic AI Systems.
- Anthropic: Core Views on AI Safety and Responsible Scaling Policy: required reading before an Anthropic mission-alignment round.
Coding fluency
- NeetCode: free structured practice for coding-under-time-pressure. FDE coding is practical, but fluency removes friction.
- OpenAI Cookbook and Anthropic Cookbook: work recipes to build muscle memory for LLM app code (retries, streaming, tool calls, RAG).
Next: prep-plan.md sequences these courses into a day-by-day path.